Overview

Dataset statistics

Number of variables17
Number of observations3189
Missing cells5051
Missing cells (%)9.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory423.7 KiB
Average record size in memory136.0 B

Variable types

NUM14
CAT2
BOOL1

Warnings

TempDist has 2506 (78.6%) missing values Missing
SpatDist has 2545 (79.8%) missing values Missing
Duration is highly skewed (γ1 = 31.7890797) Skewed
df_index has unique values Unique
AnzGesperrtFs has 985 (30.9%) zeros Zeros
Length has 906 (28.4%) zeros Zeros
Duration has 502 (15.7%) zeros Zeros

Reproduction

Analysis started2020-11-16 11:26:56.305657
Analysis finished2020-11-16 11:28:09.761997
Duration1 minute and 13.46 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct3189
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1624.501411
Minimum0
Maximum3240
Zeros1
Zeros (%)< 0.1%
Memory size24.9 KiB
2020-11-16T12:28:10.149653image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile159.4
Q1797
median1625
Q32443
95-th percentile3080.6
Maximum3240
Range3240
Interquartile range (IQR)1646

Descriptive statistics

Standard deviation941.9931113
Coefficient of variation (CV)0.5798659853
Kurtosis-1.219541929
Mean1624.501411
Median Absolute Deviation (MAD)823
Skewness-0.0127672861
Sum5180535
Variance887351.0217
MonotocityStrictly increasing
2020-11-16T12:28:10.362830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
20471< 0.1%
 
25961< 0.1%
 
25921< 0.1%
 
5411< 0.1%
 
25881< 0.1%
 
5371< 0.1%
 
25841< 0.1%
 
5331< 0.1%
 
25801< 0.1%
 
5291< 0.1%
 
Other values (3179)317999.7%
 
ValueCountFrequency (%) 
01< 0.1%
 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
41< 0.1%
 
ValueCountFrequency (%) 
32401< 0.1%
 
32391< 0.1%
 
32381< 0.1%
 
32371< 0.1%
 
32361< 0.1%
 

TempMax
Real number (ℝ≥0)

Distinct204
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean178.1185325
Minimum9
Maximum1326
Zeros0
Zeros (%)0.0%
Memory size24.9 KiB
2020-11-16T12:28:10.557272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile15
Q142
median108
Q3210
95-th percentile593.4
Maximum1326
Range1317
Interquartile range (IQR)168

Descriptive statistics

Standard deviation214.2716671
Coefficient of variation (CV)1.202972336
Kurtosis9.418214239
Mean178.1185325
Median Absolute Deviation (MAD)78
Skewness2.732664261
Sum568020
Variance45912.3473
MonotocityNot monotonic
2020-11-16T12:28:10.797037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
151554.9%
 
361153.6%
 
21832.6%
 
189792.5%
 
24732.3%
 
18682.1%
 
60621.9%
 
48601.9%
 
30541.7%
 
57501.6%
 
Other values (194)239074.9%
 
ValueCountFrequency (%) 
9431.3%
 
12421.3%
 
151554.9%
 
18682.1%
 
21832.6%
 
ValueCountFrequency (%) 
132670.2%
 
1323230.7%
 
132020.1%
 
11941< 0.1%
 
1116120.4%
 

TempAvg
Real number (ℝ≥0)

Distinct257
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.47977422
Minimum3
Maximum1326
Zeros0
Zeros (%)0.0%
Memory size24.9 KiB
2020-11-16T12:28:11.009408image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile8
Q120
median46
Q3104
95-th percentile269
Maximum1326
Range1323
Interquartile range (IQR)84

Descriptive statistics

Standard deviation105.7537384
Coefficient of variation (CV)1.282177835
Kurtosis28.15541141
Mean82.47977422
Median Absolute Deviation (MAD)31
Skewness4.022126244
Sum263028
Variance11183.85318
MonotocityNot monotonic
2020-11-16T12:28:11.229320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
151554.9%
 
162692.2%
 
31682.1%
 
18611.9%
 
17581.8%
 
30581.8%
 
9541.7%
 
12541.7%
 
21501.6%
 
6491.5%
 
Other values (247)251378.8%
 
ValueCountFrequency (%) 
330.1%
 
440.1%
 
5240.8%
 
6491.5%
 
7451.4%
 
ValueCountFrequency (%) 
132630.1%
 
96620.1%
 
9551< 0.1%
 
85820.1%
 
7931< 0.1%
 

SpatMax
Real number (ℝ≥0)

Distinct1229
Distinct (%)38.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8779.682346
Minimum699
Maximum49765
Zeros0
Zeros (%)0.0%
Memory size24.9 KiB
2020-11-16T12:28:11.624018image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum699
5-th percentile1507
Q12831
median5301
Q311685
95-th percentile27871.4
Maximum49765
Range49066
Interquartile range (IQR)8854

Descriptive statistics

Standard deviation8830.208732
Coefficient of variation (CV)1.005754922
Kurtosis4.014730331
Mean8779.682346
Median Absolute Deviation (MAD)3113
Skewness1.94738862
Sum27998407
Variance77972586.26
MonotocityNot monotonic
2020-11-16T12:28:11.859038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
28311133.5%
 
1926642.0%
 
1014391.2%
 
2908341.1%
 
2475260.8%
 
8459250.8%
 
3306240.8%
 
9045190.6%
 
2226190.6%
 
1903170.5%
 
Other values (1219)280988.1%
 
ValueCountFrequency (%) 
6991< 0.1%
 
9021< 0.1%
 
9511< 0.1%
 
9651< 0.1%
 
99120.1%
 
ValueCountFrequency (%) 
4976530.1%
 
489871< 0.1%
 
4760760.2%
 
4719630.1%
 
447511< 0.1%
 

SpatAvg
Real number (ℝ≥0)

Distinct1320
Distinct (%)41.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3198.162747
Minimum284
Maximum15602
Zeros0
Zeros (%)0.0%
Memory size24.9 KiB
2020-11-16T12:28:12.090513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum284
5-th percentile857.4
Q11536
median2231
Q33794
95-th percentile9064
Maximum15602
Range15318
Interquartile range (IQR)2258

Descriptive statistics

Standard deviation2693.584741
Coefficient of variation (CV)0.8422287902
Kurtosis4.505029754
Mean3198.162747
Median Absolute Deviation (MAD)948
Skewness2.064476875
Sum10198941
Variance7255398.758
MonotocityNot monotonic
2020-11-16T12:28:12.288147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
15361133.5%
 
1575642.0%
 
809391.2%
 
2691240.8%
 
1276230.7%
 
1683200.6%
 
1272190.6%
 
2469190.6%
 
4430140.4%
 
12363130.4%
 
Other values (1310)284189.1%
 
ValueCountFrequency (%) 
28420.1%
 
30530.1%
 
3551< 0.1%
 
4041< 0.1%
 
4191< 0.1%
 
ValueCountFrequency (%) 
1560240.1%
 
1559040.1%
 
1505440.1%
 
1478530.1%
 
1477630.1%
 

TempDist
Real number (ℝ≥0)

MISSING

Distinct24
Distinct (%)3.5%
Missing2506
Missing (%)78.6%
Infinite0
Infinite (%)0.0%
Mean11.97510981
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Memory size24.9 KiB
2020-11-16T12:28:12.495217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median12
Q318
95-th percentile22.9
Maximum24
Range23
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.592070335
Coefficient of variation (CV)0.5504809926
Kurtosis-1.11501919
Mean11.97510981
Median Absolute Deviation (MAD)6
Skewness0.1044793866
Sum8179
Variance43.4553913
MonotocityNot monotonic
2020-11-16T12:28:12.715258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%) 
7401.3%
 
3391.2%
 
14391.2%
 
9361.1%
 
6351.1%
 
10351.1%
 
8321.0%
 
18311.0%
 
12311.0%
 
15300.9%
 
Other values (14)33510.5%
 
(Missing)250678.6%
 
ValueCountFrequency (%) 
1210.7%
 
2290.9%
 
3391.2%
 
4220.7%
 
5220.7%
 
ValueCountFrequency (%) 
24220.7%
 
23130.4%
 
22290.9%
 
21270.8%
 
20240.8%
 

SpatDist
Real number (ℝ≥0)

MISSING

Distinct431
Distinct (%)66.9%
Missing2545
Missing (%)79.8%
Infinite0
Infinite (%)0.0%
Mean776.6568323
Minimum1
Maximum1988
Zeros0
Zeros (%)0.0%
Memory size24.9 KiB
2020-11-16T12:28:12.900300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile33.15
Q1229
median592.5
Q31275
95-th percentile1899.25
Maximum1988
Range1987
Interquartile range (IQR)1046

Descriptive statistics

Standard deviation594.9892705
Coefficient of variation (CV)0.7660903062
Kurtosis-0.9959372186
Mean776.6568323
Median Absolute Deviation (MAD)458
Skewness0.4592430553
Sum500167
Variance354012.232
MonotocityNot monotonic
2020-11-16T12:28:13.097818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
576431.3%
 
92381.2%
 
1025230.7%
 
198570.2%
 
43160.2%
 
150.2%
 
12040.1%
 
198640.1%
 
14630.1%
 
230.1%
 
Other values (421)50815.9%
 
(Missing)254579.8%
 
ValueCountFrequency (%) 
150.2%
 
230.1%
 
320.1%
 
41< 0.1%
 
520.1%
 
ValueCountFrequency (%) 
19881< 0.1%
 
198640.1%
 
198570.2%
 
198330.1%
 
19821< 0.1%
 

Coverage
Real number (ℝ≥0)

Distinct94
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.87707745
Minimum4
Maximum100
Zeros0
Zeros (%)0.0%
Memory size24.9 KiB
2020-11-16T12:28:13.295366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile14
Q127
median44
Q362
95-th percentile85
Maximum100
Range96
Interquartile range (IQR)35

Descriptive statistics

Standard deviation22.78115715
Coefficient of variation (CV)0.4965694943
Kurtosis-0.627902147
Mean45.87707745
Median Absolute Deviation (MAD)17
Skewness0.4239469838
Sum146302
Variance518.9811212
MonotocityNot monotonic
2020-11-16T12:28:13.499113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
481524.8%
 
35872.7%
 
27832.6%
 
85802.5%
 
100692.2%
 
32672.1%
 
64632.0%
 
47621.9%
 
34581.8%
 
79581.8%
 
Other values (84)241075.6%
 
ValueCountFrequency (%) 
430.1%
 
6120.4%
 
7160.5%
 
8110.3%
 
9160.5%
 
ValueCountFrequency (%) 
100692.2%
 
991< 0.1%
 
9740.1%
 
9570.2%
 
9490.3%
 

TLCar
Real number (ℝ≥0)

Distinct770
Distinct (%)24.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1503.711822
Minimum1000
Maximum1999
Zeros0
Zeros (%)0.0%
Memory size24.9 KiB
2020-11-16T12:28:13.734544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1048
Q11274
median1523
Q31751
95-th percentile1938
Maximum1999
Range999
Interquartile range (IQR)477

Descriptive statistics

Standard deviation283.685895
Coefficient of variation (CV)0.188657089
Kurtosis-1.168550157
Mean1503.711822
Median Absolute Deviation (MAD)237
Skewness-0.06694199848
Sum4795337
Variance80477.687
MonotocityNot monotonic
2020-11-16T12:28:13.922392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
15551093.4%
 
1297652.0%
 
1286391.2%
 
1152280.9%
 
1518250.8%
 
1015200.6%
 
1667200.6%
 
1788200.6%
 
1887180.6%
 
1659170.5%
 
Other values (760)282888.7%
 
ValueCountFrequency (%) 
100030.1%
 
1001100.3%
 
100350.2%
 
100430.1%
 
100550.2%
 
ValueCountFrequency (%) 
199920.1%
 
199740.1%
 
199660.2%
 
199570.2%
 
19941< 0.1%
 

TLHGV
Real number (ℝ≥0)

Distinct475
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean732.6688617
Minimum500
Maximum999
Zeros0
Zeros (%)0.0%
Memory size24.9 KiB
2020-11-16T12:28:14.082643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile513
Q1612
median736
Q3848
95-th percentile970
Maximum999
Range499
Interquartile range (IQR)236

Descriptive statistics

Standard deviation144.2077569
Coefficient of variation (CV)0.1968252841
Kurtosis-1.120456757
Mean732.6688617
Median Absolute Deviation (MAD)122
Skewness0.122928795
Sum2336481
Variance20795.87714
MonotocityNot monotonic
2020-11-16T12:28:14.240197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
7871233.9%
 
505682.1%
 
612451.4%
 
737351.1%
 
738351.1%
 
513311.0%
 
987220.7%
 
676210.7%
 
997210.7%
 
671200.6%
 
Other values (465)276886.8%
 
ValueCountFrequency (%) 
50090.3%
 
50160.2%
 
502100.3%
 
50320.1%
 
50440.1%
 
ValueCountFrequency (%) 
99960.2%
 
99820.1%
 
997210.7%
 
99670.2%
 
99560.2%
 

Strasse
Categorical

Distinct17
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size24.9 KiB
A 3
1023 
A 9
656 
A 99
312 
A 7
302 
A 96
230 
Other values (12)
666 
ValueCountFrequency (%) 
A 3102332.1%
 
A 965620.6%
 
A 993129.8%
 
A 73029.5%
 
A 962307.2%
 
A 61986.2%
 
A 931605.0%
 
A 73862.7%
 
A 92822.6%
 
A 94561.8%
 
Other values (7)842.6%
 
2020-11-16T12:28:14.396178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-16T12:28:14.658419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length3
Mean length3.322044528
Min length3

AnzGesperrtFs
Real number (ℝ)

ZEROS

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6829727187
Minimum-1
Maximum3
Zeros985
Zeros (%)30.9%
Memory size24.9 KiB
2020-11-16T12:28:15.086331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum3
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4890537744
Coefficient of variation (CV)0.7160663392
Kurtosis-0.341439851
Mean0.6829727187
Median Absolute Deviation (MAD)0
Skewness-0.7840930861
Sum2178
Variance0.2391735943
MonotocityNot monotonic
2020-11-16T12:28:15.192831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
1217268.1%
 
098530.9%
 
-1200.6%
 
2100.3%
 
320.1%
 
ValueCountFrequency (%) 
-1200.6%
 
098530.9%
 
1217268.1%
 
2100.3%
 
320.1%
 
ValueCountFrequency (%) 
320.1%
 
2100.3%
 
1217268.1%
 
098530.9%
 
-1200.6%
 

Einzug
Real number (ℝ≥0)

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.556287237
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size24.9 KiB
2020-11-16T12:28:15.321414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.64140536
Coefficient of variation (CV)0.6421052126
Kurtosis-1.269839513
Mean2.556287237
Median Absolute Deviation (MAD)1
Skewness0.6763175565
Sum8152
Variance2.694211556
MonotocityNot monotonic
2020-11-16T12:28:15.424767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
2116036.4%
 
1107133.6%
 
594329.6%
 
3140.4%
 
41< 0.1%
 
ValueCountFrequency (%) 
1107133.6%
 
2116036.4%
 
3140.4%
 
41< 0.1%
 
594329.6%
 
ValueCountFrequency (%) 
594329.6%
 
41< 0.1%
 
3140.4%
 
2116036.4%
 
1107133.6%
 

Richtung
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.9 KiB
1
3121 
0
 
68
ValueCountFrequency (%) 
1312197.9%
 
0682.1%
 
2020-11-16T12:28:15.512235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length
Real number (ℝ≥0)

ZEROS

Distinct1406
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean944.3270618
Minimum0
Maximum24500
Zeros906
Zeros (%)28.4%
Memory size24.9 KiB
2020-11-16T12:28:15.620449image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median299
Q31235
95-th percentile3896.4
Maximum24500
Range24500
Interquartile range (IQR)1235

Descriptive statistics

Standard deviation1680.502881
Coefficient of variation (CV)1.779577171
Kurtosis34.70304946
Mean944.3270618
Median Absolute Deviation (MAD)299
Skewness4.529227434
Sum3011459
Variance2824089.935
MonotocityNot monotonic
2020-11-16T12:28:15.811784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
090628.4%
 
100190.6%
 
200130.4%
 
150110.3%
 
300100.3%
 
6590.3%
 
50590.3%
 
6680.3%
 
5080.3%
 
5280.3%
 
Other values (1396)218868.6%
 
ValueCountFrequency (%) 
090628.4%
 
81< 0.1%
 
101< 0.1%
 
2620.1%
 
2720.1%
 
ValueCountFrequency (%) 
245001< 0.1%
 
210321< 0.1%
 
174301< 0.1%
 
168201< 0.1%
 
141851< 0.1%
 

Duration
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct505
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean323.5484478
Minimum0
Maximum187650
Zeros502
Zeros (%)15.7%
Memory size24.9 KiB
2020-11-16T12:28:15.986804image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median27
Q393
95-th percentile535
Maximum187650
Range187650
Interquartile range (IQR)89

Descriptive statistics

Standard deviation4542.480857
Coefficient of variation (CV)14.03956931
Kurtosis1156.41457
Mean323.5484478
Median Absolute Deviation (MAD)27
Skewness31.7890797
Sum1031796
Variance20634132.34
MonotocityNot monotonic
2020-11-16T12:28:16.154129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
050215.7%
 
11143.6%
 
4922.9%
 
3862.7%
 
2832.6%
 
5551.7%
 
7531.7%
 
9471.5%
 
12411.3%
 
14391.2%
 
Other values (495)207765.1%
 
ValueCountFrequency (%) 
050215.7%
 
11143.6%
 
2832.6%
 
3862.7%
 
4922.9%
 
ValueCountFrequency (%) 
1876501< 0.1%
 
1320601< 0.1%
 
753301< 0.1%
 
391801< 0.1%
 
3213020.1%
 

Month
Categorical

Distinct12
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size24.9 KiB
Jul
529 
Sep
370 
Oct
307 
Dec
278 
Aug
273 
Other values (7)
1432 
ValueCountFrequency (%) 
Jul52916.6%
 
Sep37011.6%
 
Oct3079.6%
 
Dec2788.7%
 
Aug2738.6%
 
Apr2718.5%
 
May2688.4%
 
Mar2166.8%
 
Nov2136.7%
 
Jun1865.8%
 
Other values (2)2788.7%
 
2020-11-16T12:28:16.335737image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-16T12:28:16.485505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Interactions

2020-11-16T12:27:02.142648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:03.556545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:04.958895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:06.428906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:07.930101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:09.587725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:11.151849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:12.763837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:14.237466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:15.700076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:17.118761image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:18.574221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:20.217048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:21.820966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:24.161269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:24.182570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:24.300379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:24.406507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:24.529671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:24.650321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:24.769440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:24.900571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:25.015174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:25.140017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:25.260989image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:26.157797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:26.302413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:26.417660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:28.004808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:28.026113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:28.138778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:28.239865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:28.349910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:28.459801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:28.559670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:28.677363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:28.783459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:28.893146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:28.997096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:29.110765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:29.231762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:29.343535image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:30.921719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:30.943865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:31.046088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:31.138621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:31.230394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:31.333513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:31.439313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:31.552409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:31.645199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:31.746027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:31.858728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:31.967464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:32.079330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:32.189038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:33.783862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:33.806272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:33.933941image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:34.050526image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:34.167019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:34.287783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:34.402373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:34.520768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:34.621350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:34.730592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:34.839616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:34.947388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:35.063505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:35.173156image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:36.686759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:36.708654image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:36.828938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:36.940102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:37.047004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:37.166176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:37.269009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:37.383508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:37.479892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:37.584464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:37.689921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:37.795703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:37.909023image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:38.018056image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:39.500277image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:39.520897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:39.639088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:39.749646image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:39.864634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:39.982551image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:40.099613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:40.224137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:40.350608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:40.472949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:40.589861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:40.706781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:40.840101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:40.971031image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:42.493212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:42.515123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:42.631544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:42.737113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:42.842514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:42.953664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:43.059899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:43.177753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:43.281252image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:43.388789image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:43.495628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:43.597436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:43.707341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:43.808376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:45.338399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:45.359871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:45.470717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:45.573225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:45.671508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:45.779224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:45.889137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:46.005503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:47.435203image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:47.545352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:47.658149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:47.767730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:47.888001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:47.993533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:49.479651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:49.502632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:49.611701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:49.717636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:49.828262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:49.955209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:50.070974image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:50.194620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:50.303055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:50.418363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:50.535455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:50.649766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:50.774652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:50.902565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:52.694221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:52.714643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:52.834714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:52.941120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:53.042778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:53.154193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:53.258103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:53.377489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:53.496106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:53.620645image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:53.742361image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:53.865563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:53.990580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:54.256819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:55.807630image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:55.828794image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:55.954792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:56.067320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:56.186398image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:56.308154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:56.424760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:56.554945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:56.671680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:56.786491image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:56.910754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:57.031451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:57.162760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:57.291785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:58.794404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:58.816992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:58.947905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:59.065963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:59.201122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:59.358749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:59.475669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:59.600379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:59.717307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:59.825625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:27:59.942018image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:28:00.053937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:28:00.176813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:28:00.297615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:28:01.951872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:28:01.974994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:28:02.261589image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:28:02.378051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:28:02.493817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:28:02.614441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:28:02.732534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:28:02.867467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:28:02.983058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:28:03.101223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:28:03.222218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:28:03.343469image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:28:03.473671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:28:03.593252image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-11-16T12:28:18.106776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-16T12:28:19.653636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-16T12:28:21.105169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-16T12:28:22.540506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-11-16T12:28:22.585458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-11-16T12:28:05.802207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:28:07.430555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:28:07.892964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-16T12:28:09.730189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

df_indexTempMaxTempAvgSpatMaxSpatAvgTempDistSpatDistCoverageTLCarTLHGVStrasseAnzGesperrtFsEinzugRichtungLengthDurationMonth
006930151833134NaNNaN181966954A 9121018Jan
116930151833134NaNNaN181966954A 91111377247Jan
226930151833134NaNNaN181966954A 90516361Jan
3333653011317NaNNaN171131856A 9121410Jan
4433653011317NaNNaN171131856A 9051260345Jan
5533653011317NaNNaN171131856A 90512588Jan
661741561543213788NaNNaN881388591A 71111322143Jan
77572322101205NaNNaN511143580A 701213246407Jan
8857232210120518.01253.0511143580A 701215053Jan
992492521022510425NaN258.01001508981A 711111005199Jan

Last rows

df_indexTempMaxTempAvgSpatMaxSpatAvgTempDistSpatDistCoverageTLCarTLHGVStrasseAnzGesperrtFsEinzugRichtungLengthDurationMonth
3179323175356554293413.01341.0431853799A 3111102822Dec
31803232753565542934NaN702.0431853799A 305163920Dec
31813233816126982360NaNNaN861305511A 711158560Dec
31823234816126982360NaNNaN861305511A 70518511Dec
31833235816126982360NaNNaN861305511A 7-11130075Dec
318432366017122194654NaNNaN371671871A 905111611Dec
318532376017122194654NaNNaN371671871A 91213054Dec
318632386017122194654NaNNaN371671871A 90511291Dec
3187323960389514908.0NaN421862595A 960505225Dec
3188324039212218967NaNNaN421003565A 73121151839Dec